--- title: Title keywords: fastai sidebar: home_sidebar nb_path: "network_embeding.ipynb" ---
from gensim.models.doc2vec import Doc2Vec
model = Doc2Vec.load('zika.d2v')
SN = SemanticNetwork(10,0.7,model)
vecs = SN.get_vectors()
clust = SN.get_agglomerative_cluster(vecs)
SN.grow_network(vecs)
SN.net[16][100]
communities = greedy_modularity_communities(SN.net, weight='weight', n_communities=100, resolution=3)
# communities = [c for c in girvan_newman(SN.net)]
# communities = [c for c in asyn_lpa_communities(SN.net, weight='weight')]
print(f'Found {len(communities)} communities')
print(len(communities[0]))
plt.hist([len(c) for c in communities if len(c)>1], bins=10);
plot_network(SN.net.subgraph(communities[0]))
import seaborn as sns
g = sns.clustermap(vecs, method='complete', metric='cosine', figsize =(15,35));
doc_linkage = g.dendrogram_row.linkage
dists = pdist(vecs,metric='cosine')
cs = get_clusters(doc_linkage,dists, 0.6)
Counter(cs)
fig, ax = plt.subplots(1,1, figsize =(15,10))
den = dendrogram(doc_linkage,color_threshold=0.6, ax=ax)